Fusion Learning for 1-Bit CS-based Superimposed CSI Feedback with Bi-Directional Channel Reciprocity

TL;DR

This paper introduces a fusion learning scheme leveraging bi-directional channel reciprocity to enhance 1-bit CS-based superimposed CSI feedback, significantly improving reconstruction accuracy and reducing processing delay.

Contribution

It proposes a novel fusion learning framework with amplitude-learning and fusion networks, addressing low accuracy and high delay issues in existing 1-bit CSI feedback methods.

Findings

Improved CSI reconstruction accuracy demonstrated in simulations.

Reduced processing delay compared to traditional methods.

Robustness against parameter variations confirmed.

Abstract

Due to the discarding of downlink channel state information (CSI) amplitude and the employing of iteration reconstruction algorithms, 1-bit compressed sensing (CS)-based superimposed CSI feedback is challenged by low recovery accuracy and large processing delay. To overcome these drawbacks, this letter proposes a fusion learning scheme by exploiting the bi-directional channel reciprocity. Specifically, a simplified version of the conventional downlink CSI reconstruction is utilized to extract the initial feature of downlink CSI, and a single hidden layer-based amplitude-learning network (AMPL-NET) is designed to learn the auxiliary feature of the downlink CSI amplitude. Then, based on the extracted and learned amplitude features, a simple but effective amplitude-fusion network (AMPF-NET) is developed to perform the amplitude fusion of downlink CSI and thus improves the reconstruction accuracy for 1-bit CS-based superimposed CSI feedback while reducing the processing delay. Simulation results show the effectiveness of the proposed feedback scheme and the robustness against parameter variations.